AI DRIVEN ENERGY EFFICIENT COMPUTING: TECHNIQUES, HARDWARE INNOVATIONS AND SUSTAINABILITY CHALLENGES

The integration of Artificial Intelligence (AI) in areas like healthcare, finance, and cloud computing has notably heightened computational needs and energy usage, prompting important sustainability issues. Data centers and AI-driven tasks now represent a significant share of worldwide electricity consumption, requiring creative strategies to enhance energy efficiency while maintaining performance. This paper presents an in-depth analysis of AI-driven energy-efficient computing, exploring the difficulties posed by AI tasks and the potential AI brings for enhancing power efficiency. Crucial AI methods, such as smart workload scheduling, forecast power management, dynamic voltage and frequency adjustment, and flexible resource distribution, are examined for their efficiency in minimizing energy consumption in computing systems. This paper also examines hardware advancements like AI accelerators, energy-efficient processors, cutting-edge memory architectures, and edge computing devices that enhance AI-driven optimization. Emerging paradigms such as hardware–software co-design, neuromorphic computing, and energy-efficient interconnects are also examined. In conclusion, the paper emphasizes key obstacles concerning scalability, energy expenses for training, complexity of models, and trade-offs between performance and energy, proposing future research paths for sustainable and energy-efficient AI systems.